LTI–PCS–EPUSP nic-wscg2011 N. Werneck 1–Introduction 2–Methodology 3–Results References Referˆ encias c N. Werneck Speeding up probabilistic inference of camera orientation by function approximation and grid masking Nicolau L. Werneck Doctoral candidate Supervisor: Prof. Anna Helena Reali Costa Intelligent Techniques Laboratory, LTI — PCS — Poli Universidade de S˜ ao Paulo (USP), Brazil WSCG’2011, Plzen Feb/2011 1 / 15
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Speeding up probabilistic inference of camera orientation by function approximation and grid masking - WSCG2011 presentation
Slides from my presentation at the WSCG2011. Describes some modifications to existing techniques for camera orientation estimation in "Manhattan Worlds" aiming at faster calculation times.
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Expressions were implemented in Cython, using SIMDinstructions, and tested on c1.xlarge AWS computers.A speedup of 50–64× was detected.
Original 1100.0 ±60msProposed 18.9 ±2.4ms
(4s per image with the proposal, without subsampling.)
Quality
From 102 tests, the original expression “fixed” thesolution in 5 occasions, but ruined 6 good solutions.Mean error went from 4.7◦ to 5.5◦. (Large outliers)
James M. Coughlan and A. L. Yuille. Manhattan world: orientationand outlier detection by bayesian inference. Neural Comput.,15(5):1063–1088, 2003. ISSN 0899-7667. URLdoi:10.1162/089976603765202668.
Patrick Denis, James H. Elder, and Francisco J. Estrada. Efficientedge-based methods for estimating manhattan frames in urbanimagery. In David A. Forsyth, Philip H. S. Torr, and AndrewZisserman, editors, ECCV (2), volume 5303 of Lecture Notesin Computer Science, pages 197–210. Springer, 2008. ISBN978-3-540-88685-3.
Jonathan Deutscher, Michael Isard, and John Maccormick.Automatic camera calibration from a single manhattan image.In Eur. Conf. on Computer Vision (ECCV, pages 175–205,2002.
Grant Schindler and Frank Dellaert. Atlanta world: An expectationmaximization framework for simultaneous low-level edgegrouping and camera calibration in complex man-madeenvironments. In CVPR (1), pages 203–209, 2004.